Advances in sensor technology have made it possible for sensors to provide significant amounts of information concerning an area of interest. For example, wide-range sensors can be mounted to high-altitude aircraft or orbital satellites to provide a comprehensive view of the area. These sensors can provide continuous monitoring of events occurring within the area of interest, but generally, view so many events that it is not always easy to discern which events are of interest, or easily identify events caused by affiliated sources. In some applications, mobile short-range sensors, focused area sensors, or human investigators can be made available for further investigation of a given event, but the sheer number of events generally detected within a given area can make it difficult to allocate these resources effectively, or to provide adequate discernment thereby.
Under the prior state of the art, extraction of useful decision making information from the sensor data can be a difficult process. The various sensors work independently of other available information, making it difficult to distinguish (without significant human intervention) interesting events from events already known by decision makers. This intervention adds to the expense of collecting data, introduces the possibility of human error, and, most importantly, significantly delays the interpretation and enhancement of the collected data. Since the cause of an event may not be apparent even shortly after the occurrence of the event, it is important to quickly and decisively appreciate the importance of an event, and gather desired information while the information is available.
When dealing with these situations in the past, it has been the duty of human operators to determine meaning from provided sensor data. The sensor readings are analyzed by a staff of individuals in light of available knowledge of the area of interest, and sensor resources are allocated to further investigate events on an ad hoc basis. Amongst other problems, the decision-making staff is faced with the problem of optimizing the allocation of sensor resources to interesting events. The number of events occurring within an area of interest at any given time generally exceeds the available sensor resources, such that a desirable level of sensor resources can generally not be brought to bear on every detected event. As the number of sensor systems and detected events increase, such determinations become increasingly difficult.